Time Collection Prediction Techniques of Sciences in Education Using ARIMA Model Instance from Technical Establishments Case of RP-KIGALI-College

This study examines the sizeable ability of time series forecasting science in education zone. Through applying ARIMA, Exponential Smoothing, and Seasonal Decomposition fashions to real-world statistics on pupil enrollment, instructional overall performance, attendance, and teacher retention, the look at provides actionable insights for educators, policymakers, and directors. Many researcher research in make the studies at excessive gaining knowledge of organization (RP-Kigali college, TUMBA, MUSANZE, HUYE), they did now not display the case why college students want to fail Sciences in Technical college? what is the reality motive them to fail science? In my research I got here into technical school students want to lose relies upon on the reality that the wide variety of college students is inserted inside the technical college is half of’s within the Sciences, it makes the number of the winners are rather relatively averaged whilst enters the technical faculty have a lowest know-how of the Sciences. My contribution lies in providing personalized, engaging and data driven solutions that bridge the gap between technical training and scientific learning, helping students succeed in both fields. This study come up by the result showing that: The successive three years:2024: 82%, 2025: 85%, 2026: 87%. There is sluggish improvement in common test ratings in science at PR KIGALI College High learning Institution, which shows that recent educational reforms or curriculum modifications are having a superb effect.